Mathematical modelling Books
Springer Nature Switzerland AG Managing Engineered Assets: Principles and
Book SynopsisThis textbook deals with engineering, science, technical, legal, financial, ICT, logistics and people management topics necessary for managing engineered assets such as all man-made tools, gadgets, buildings, equipment, machines, infrastructure, large-scale physical and industrial facilities and systems which pervade all sectors of industry. By coalescing concepts, principles, practices, and practical issues from the relevant multi-disciplines, the book addresses the body of knowledge required for managing engineered assets in the 4IR and Society 5.0 era and beyond.The book is written for: Scholars and students who intend to strengthen or acquire knowledge about the concepts, principles, and practice of managing engineered assets; Managers of engineered assets in both the public and private sectors who aim to improve asset management practice for their organisational purposes and missions; Policymakers and regulators in order to improve policymaking, governance, assessment and evaluation frameworks on the management of engineered assets; The broader audience concerned about the sustainable management of engineered assets that constitute our built environment and provide the means for industry and livelihood. Table of ContentsDefinitions and Scope.- Value and Sustainability.- Technical Principles.- Practical Concepts.- Engineering Asset Management Framework.- Asset Acquisition Stage.- Asset Utilisation Stage.- Asset Retirement Stage.- SAMP and EAMBoK.
£71.24
Springer Nature Switzerland AG Measuring Professional Competence for the
Book SynopsisThis open access book presents a structural model and an associated test instrument designed to provide a detailed analysis of professional competences for teaching mathematical modelling. The conceptualisation is based on the COACTIV model, which describes aspects, areas and facets of professional competences of teachers. The manual provides an overview of the essential teaching skills in application-related contexts and offers the tools needed to capture these aspects. It discusses the objectives and application areas of the instrument, as well as the development of the test. In addition, it describes the implementation and evaluates the quality and results of the structural equation analysis of the model. Teaching mathematical modelling is a cognitively challenging activity for (prospective) teachers. Thus, teacher education requires a detailed analysis of professional competence for teaching mathematical modelling. Measuring this competence requires theoretical models that accurately describe requirements placed upon teachers, as well as appropriate evaluation tools that adequately capture skills and abilities in this field. This book presents an instrument that measures the professional competences in a sample of 349 prospective teachers.Table of ContentsIntroduction.- Objectives and application areas.- Test development.- Implementation of the test.- Test quality.- Selected results.- References.- Attachment.- Modelling experiences.- Beliefs about mathematical modelling.- Self-efficacy about assesing mathematical modelling.- Modelling specific pedagogical content knowledge.- Test booklet.
£44.99
Springer Nature Switzerland AG Kernel Mode Decomposition and the Programming of Kernels
Book SynopsisThis monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework.Although kernel methods have strong theoretical foundations, they require the prior selection of a good kernel. While the usual approach to this kernel selection problem is hyperparameter tuning, the objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the context of additive Gaussian processes.It is suitable for engineers, computer scientists, mathematicians, and students in these fields working on kernel methods, pattern recognition, and mode decomposition problems.Table of ContentsIntroduction.- Review.- The mode decomposition problem.- Kernel mode decomposition networks (KMDNets).- Additional programming modules and squeezing.- Non-trigonometric waveform and iterated KMD.- Unknown base waveforms.- Crossing frequencies, vanishing modes, and noise.- Appendix.
£59.99
Springer QPLEX A Computational Modeling and Analysis Methodology for Stochastic Systems
Book Synopsis.- Preliminaries..- First Look at QPLEX..- Part 1 QPLEX Modeling and Calculus..- Introduction to QPLEX Modeling and Calculus..- Simple Transition Dynamics..- Models with Simple Transition Dynamics..- Advanced Transition Dynamics..- Models with Advanced Transition Dynamics..- Conditional and Joint Probabilities..- Part 2 Graphical QPLEX Calculus..- Introduction to Graphical QPLEX Calculus..- Subsystem QPLEX Calculus..- Conditional Independence..- Information Structure..- Graphical QPLEX Calculus with Distributional Programs..- Efficient Calculation for Distributional Programs..- Part 3 Foundations..- Introduction to Foundations..- Optimality of QPLEX Iterates..- Exactness Results.
£44.99
Springer Mindmatics
Book SynopsisPreface.- The Root of Mind and Mathematics.- Equality, Similarity, and Transformations.- Mind and Mathematics in an Event-Centered Approach.- Symmetry, the Unconscious, and Imagination.- Imagination, Mathematics, and Mysticism.- Epilogue.- Index.
£49.49
Springer Image Schema Theory and Mathematical Cognition
Book SynopsisPreface.- The Starting Point: Lakoff and Núñez.- Image Schema Theory.- Related Processes.- Learning, Diagrams, and AI.- Index.
£44.99
Springer International Publishing AG Mathematical Modeling of Lithium Batteries: From Electrochemical Models to State Estimator Algorithms
Book Synopsis This book is unique to be the only one completely dedicated for battery modeling for all components of battery management system (BMS) applications. The contents of this book compliment the multitude of research publications in this domain by providing coherent fundamentals. An explosive market of Li ion batteries has led to aggressive demand for mathematical models for battery management systems (BMS). Researchers from multi-various backgrounds contribute from their respective background, leading to a lateral growth. Risk of this runaway situation is that researchers tend to use an existing method or algorithm without in depth knowledge of the cohesive fundamentals—often misinterpreting the outcome. It is worthy to note that the guiding principles are similar and the lack of clarity impedes a significant advancement. A repeat or even a synopsis of all the applications of battery modeling albeit redundant, would hence be a mammoth task, and cannot be done in a single offering. The authors believe that a pivotal contribution can be made by explaining the fundamentals in a coherent manner. Such an offering would enable researchers from multiple domains appreciate the bedrock principles and forward the frontier.Battery is an electrochemical system, and any level of understanding cannot ellipse this premise. The common thread that needs to run across—from detailed electrochemical models to algorithms used for real time estimation on a microchip—is that it be physics based. Build on this theme, this book has three parts. Each part starts with developing a framework—often invoking basic principles of thermodynamics or transport phenomena—and ends with certain verified real time applications. The first part deals with electrochemical modeling and the second with model order reduction. Objective of a BMS is estimation of state and health, and the third part is dedicated for that. Rules for state observers are derived from a generic Bayesian framework, and health estimation is pursued using machine learning (ML) tools. A distinct component of this book is thorough derivations of the learning rules for the novel ML algorithms. Given the large-scale application of ML in various domains, this segment can be relevant to researchers outside BMS domain as well.The authors hope this offering would satisfy a practicing engineer with a basic perspective, and a budding researcher with essential tools on a comprehensive understanding of BMS models.Table of ContentsLithium batteries and underlying electrochemical processes.- Electrochemical model (EM) for lithium batteries.- Electrochemical impedance spectroscopy (EIS) models.- Equivalent circuit models (ECM).- Reduced order models.- Battery management system – state estimator and algorithms.- Battery thermal models.- Battery life models.
£54.99
Springer International Publishing AG A User’s Guide to Network Analysis in R
Book SynopsisPresenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.Table of ContentsIntroducing Network Analysis in R.- The Network Analysis "5 Number Summary".- Network Data Management in R.- Basic Network Plotting and Layout.- Effective Network Graphic Design.- Advanced Network Graphics.- Actor Prominence.- Subgroups.- Affiliation Networks.- Random Network Models.- Statistical Network Models.- Dynamic Network Models.- Simulations.
£59.99
Springer International Publishing AG Quality Control with R: An ISO Standards Approach
Book SynopsisPresenting a practitioner's guide to capabilities and best practices of quality control systems using the R programming language, this volume emphasizes accessibility and ease-of-use through detailed explanations of R code as well as standard statistical methodologies. In the interest of reaching the widest possible audience of quality-control professionals and statisticians, examples throughout are structured to simplify complex equations and data structures, and to demonstrate their applications to quality control processes, such as ISO standards. The volume balances its treatment of key aspects of quality control, statistics, and programming in R, making the text accessible to beginners and expert quality control professionals alike. Several appendices serve as useful references for ISO standards and common tasks performed while applying quality control with R. Trade Review Table of Contents
£52.49
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Selected Applications of Convex Optimization
Book SynopsisThis book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.Trade Review“Selected Applications of Convex Optimization is a brief book, only 140 pages, and includes exercises with each chapter. It would be a good supplemental text for an optimization or machine learning course.” (John D. Cook, MAA Reviews, maa.org, December, 2015)Table of ContentsPreliminary Knowledge.- Support Vector Machines.- Parameter Estimations.- Norm Approximation and Regulariztion.- Semi-Definite Programing and Linear Matrix Inequalities.- Convex Relaxation.- Geometric Problems.
£44.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imperfect Information and Investor Heterogeneity in the Bond Market
Book SynopsisReal world investors differ in their tastes and attitudes and they do not have, in general, perfect information about the future prospects of the economy. Most theoretical models, however, assume to the contrary that investors are homogeneous and perfectly informed about the market. In this book, an attempt is made to overcome these shortcomings. In three different case studies, the effect of heterogeneous time preferences, heterogeneous beliefs and imperfect information about the economy's growth on the term structure of interest rates are studied. The initial chapter gives an introduction to the theory of financial markets in continuous time under imperfect information and establishes the existence of an equilibrium with complete markets.Table of ContentsInformation.- Equilibrium with Imperfect Information and Complete Asset Markets in Continuous Time: Introduction; A Competitive Financial Market with Imperfect Information; Martingale Representation Theorem for the Innovation Process; The Existence of an Arrow-Debreu Equilibrium, Pareter Efficiency and the Representation Agent; Completeness of the Market and Existence of a Financial Equilibrium; Pricing Redundant Securities and the Term Structure of Interest Rates.- Heterogeneous Time Preferences - Preferred Habitat Theory Revisited: Modeling Preferred Habitat Time Preferences; A Model with Heterogeneous Time Preferences; Equilibrium; Analysis of the Term Structure; The Demand for Long-Term Bonds.- Imperfect Information: The Term Structure when the Growth Rate is Unknown: The Model; Estimating the Drift; Equilibrium with Perfect and Imperfect Information; The Yield Curve with Normal and Bernoulli Prior Beliefs; General Prior Beliefs.- Bulls and Bears: Heterogeneous Expectations: Setup; Equilibrium; Two Examples.
£44.99
Springer Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics
Book SynopsisThis book deals with the application of spectral methods to problems of uncertainty propagation and quanti?cation in model-based computations. It speci?cally focuses on computational and algorithmic features of these methods which are most useful in dealing with models based on partial differential equations, with special att- tion to models arising in simulations of ?uid ?ows. Implementations are illustrated through applications to elementary problems, as well as more elaborate examples selected from the authors’ interests in incompressible vortex-dominated ?ows and compressible ?ows at low Mach numbers. Spectral stochastic methods are probabilistic in nature, and are consequently rooted in the rich mathematical foundation associated with probability and measure spaces. Despite the authors’ fascination with this foundation, the discussion only - ludes to those theoretical aspects needed to set the stage for subsequent applications. The book is authored by practitioners, and is primarily intended for researchers or graduate students in computational mathematics, physics, or ?uid dynamics. The book assumes familiarity with elementary methods for the numerical solution of time-dependent, partial differential equations; prior experience with spectral me- ods is naturally helpful though not essential. Full appreciation of elaborate examples in computational ?uid dynamics (CFD) would require familiarity with key, and in some cases delicate, features of the associated numerical methods. Besides these shortcomings, our aim is to treat algorithmic and computational aspects of spectral stochastic methods with details suf?cient to address and reconstruct all but those highly elaborate examples.Table of ContentsIntroduction: Uncertainty Quantification and Propagation.- Basic Formulations.- Spectral Expansions.- Non-intrusive Methods.- Galerkin Methods.- Detailed Elementary Applications.- Application to Navier-Stokes Equations.- Advanced topics.- Solvers for Stochastic Galerkin Problems.- Wavelet and Multiresolution Analysis Schemes.- Adaptive Methods.- Epilogue.
£71.24
Bloomsbury Publishing (UK) Neural Networks Grassroots
Book SynopsisPHIL PICTON is a Reader in Engineering Control Systems at Nene College in Northampton. Prior to this he was a lecturer at the Open University where he contributed to distance learning courses on control engineering, electronics, mechatronics and artificial intelligence. His research interests include patten recognition, intelligent control and logic design.
£73.27
Taylor & Francis Ltd hyperbolicsetsshadowingandpersistencefornoninverti
Book SynopsisThis text gives a self-contained and detailed treatment of presently known results, and new theorems on hyperbolicity, shadowing, complicated motion, and robustness. The book is intended to provide a dependable reference for researchers wishing to apply such results. This book will be of particular interest to researchers and students interested in dynamical systems, particularly in noninvertible maps and infinite dimensional semi-flows or maps and global analysis.Table of ContentsIntroductionHyperbolic operators, projections , diagonalizationSequence spaces and substitution operatorsHyperbolic setsShadowing and persistence of hyperbolic setsTransversal homoclinic orbitsAppendixNotationReferences
£161.50
Taylor & Francis Inc Mathematical Models for Structural Reliability
Book SynopsisMathematical Models for Structural Reliability Analysis offers mathematical models for describing load and material properties in solving structural engineering problems. Examples are provided, demonstrating how the models are implemented, and the limitations of the models are clearly stated. Analytical solutions are also discussed, and methods are clearly distinguished from models. The authors explain both theoretical models and practical applications in a clear, concise, and readable fashion.Table of ContentsStochastic Process Models (F. Casciati and M. Di Paola)IntroductionThe Orthogonal-Increment ModelThe Correlation-Stationary Model Time-Invariant Linear Systems Models of Common UseThe Evolutionary Model Time-Invariant Linear SystemsMarkov Processes A Model of Common Use Itô Stochastic Differential Equation Some Examples Approximation of Mechanical Processes: Physical versus Itô EquationsThe Random Pulse Train Model The Delta-Correlated Model Fokker Planck and Moment Equations for Parametric Delta Correlated Input Quasi-Linear Systems Simulation of Delta Correlated Processes and Response Simulation of Normal White Noise Input and Response Orthogonal-Increment Model for Delta Correlated ProcessesMultidegree-of-Freedom Systems Under Parametric Delta Correlated Input Moment Equation Approach for MDOF Systems Simulation of Multivariate Delta Correlated Processes and ResponseConclusions and ReferencesAppendix Characterization of Random Variables Joint Characterization of Random Variables Operation on Stochastic Processes Kronecker Algebra: Some FundamentalsDimension Reduction and Discretization in Stochastic Problems by Regression Method (O. Ditlevsen)IntroductionLinear RegressionNormal DistributionNon-Gaussian Distributions and Linear RegressionMarginally Transformed Gaussian Processes and FieldsDiscretized Fields Defined by Linear Regression on a Finite Set of Field ValuesDiscretization Defined by Linear Regression on a Finite Set of Linear FunctionalsPoisson Load Field ExampleStochastic Finite Element Methods and Reliability CalculationsClassical versus Statistical-Stochastic Interpolation Formulated on the Basis of the Principle of Maximum LikelihoodComputational Practicability of the Statistical-Stochastic Interpolation MethodField Modeling on the Basis of Measured Noisy DataDiscretization Defined by L
£194.75
Taylor & Francis Ltd Reflexion and Control Mathematical Models 5
Book SynopsisThis book is dedicated to modern approaches to mathematical modeling of reflexive processes in control. The authors consider reflexive games that describe the gametheoretical interaction of agents making decisions based on a hierarchy of beliefs regarding (1) essential parameters (informational reflexion), (2) decision principles used by opponents (strategic reflexion), (3) beliefs about beliefs, and so on. Informational and reflexive equilibria in reflexive games generalize a series of well-known equilibrium concepts in noncooperative games and models of collective behavior. These models allow posing and solving the problems of informational and reflexive control in organizational, economic, social and other systems, in military applications, etc. (the interested reader will find in the book over 30 examples of possible applications in these fields) and describing uniformly many psychological/sociological phenomena connected with reflexion, viz., implicit control, informational control via the mass media, reflexion in chess, art works, etc. The present book is intended for experts in decision making and control of systems of an interdisciplinary nature, as well as for undergraduates and postgraduates.Table of ContentsIntroduction. 1. Reflexion in decision-making 2. Informational reflexion and control 3. Strategic reflexion and control 4. Applied models of informational and reflexive control. Conclusion.
£104.50
Taylor & Francis Ltd Stochastic Communities
Book SynopsisStochastic Communities presents a theory of biodiversity by analyzing the distribution of abundances among species in the context of a community. The basis of this theory is a distribution called the J distribution. This distribution is a pure hyperbola and mathematically implied by the stochastic species hypothesis assigning equal probabilities of birth and death within the population of each species over varying periods of time. The J distribution in natural communities has strong empirical support resulting from a meta-study and strong theoretical support from a theorem that is mathematically implied by the stochastic species hypothesis.Trade Review"The science of ecology suffers from a disconnect between theory and direct observation. Mathematicians have thought that simple equations could explain ecology. Field ecologists have assumed the mathematicians are right. Thus, empirical ecological understanding and prediction have suffered. Dewdney is an exceptions; he does field work and he is a mathematician. In his wonderful book, he takes advantage of both parts of ecology. And if Dewdney has done his math right, this book opens a whole new door to understanding biodiversity and its myriad causes."- Daniel Botkin, Professor Emeritus in the Department of Ecology, Evolution, and Marine Biology at the University of California, Santa Barbara, and President of the Center for the Study of the Environment"… development of a theory and guide to sampling, … it will be of great interest to both empirical and theoretical ecologists." - Trends in Ecology and EvolutionTable of ContentsThe J-curve and the J distribution. The J-distribution and its variations. Sampling in practice and in theory. Compiling and analysing field data. Predictions from data. Extending the sample. Stochastic systems and the stochastic community. The metastudy: A review. Fossil J-curves. Summary of theory and open problems. Appendix A: Mathematical Notes and Computer Tools. Appendix B: Results of the metastudy for the J distribution. Appendix C: Results of the test for the J distribution in taxonomic data.
£166.25
Taylor & Francis Inc Stochastic Modeling for Medical Image Analysis
Book SynopsisStochastic Modeling for Medical Image Analysis provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis.Today, image-guided computer-assisted diagnostics (CAD) faces two basic challenging problems. The first is the computationally feasible and accurate modeling of images from different modalities to obtain clinically useful information. The second is the accurate and fast inferring of meaningful and clinically valid CAD decisions and/or predictions on the basis of model-guided image analysis.To help address this, this book details original stochastic appearance and shape models with computationally feasible and efficient learning techniques for improving the performance of object detection, segmentation, alignment, and analysis in a number of important CAD applications.The book demonstrates accurate descriptions of visual appearances and shapes of the goal objects and their background to help solve aTable of ContentsMedical Imaging Modalities. From Images to Graphical Models. IRF Models: Estimating Marginals. Markov-Gibbs Random Field Models: Estimating Signal Interactions. Applications: Image Alignment. Segmenting Multimodal Images. Segmenting with Deformable Models. Segmenting with Shape and Appearance Priors. Cine Cardiac MRI Analysis. Sizing Cardiac Pathologies.
£171.00
Taylor & Francis Inc Moving Finite Element Method
Book SynopsisThis book focuses on process simulation in chemical engineering with a numerical algorithm based on the moving finite element method (MFEM). It offers new tools and approaches for modeling and simulating time-dependent problems with moving fronts and with moving boundaries described by time-dependent convection-reaction-diffusion partial differential equations in one or two-dimensional space domains. It provides a comprehensive account of the development of the moving finite element method, describing and analyzing the theoretical and practical aspects of the MFEM for models in 1D, 1D+1d, and 2D space domains. Mathematical models are universal, and the book reviews successful applications of MFEM to solve engineering problems. It covers a broad range of application algorithm to engineering problems, namely on separation and reaction processes presenting and discussing relevant numerical applications of the moving finite element method derived from real-world process simulations.Table of Contents1. Modeling and Simulation in Chemical Engineering. 2. The Moving Finite Elements Method. 3. Solving 1D Time-Dependent Models. 4. Solving 2D Time-Dependent Problems. 5. Solving Two Scales 1D+1d Time-Dependent Problems. 6. Solving Moving Boundary Problems. 7. Looking Ahead. 8. Index
£114.00
Taylor & Francis Inc Functional and Impulsive Differential Equations
Book SynopsisThe book presents qualitative results for different classes of fractional equations, including fractional functional differential equations, fractional impulsive differential equations, and fractional impulsive functional differential equations, which have not been covered by other books. It manifests different constructive methods by demonstrating how these techniques can be applied to investigate qualitative properties of the solutions of fractional systems. Since many applications have been included, the demonstrated techniques and models can be used in training students in mathematical modeling and in the study and development of fractional-order models.Table of ContentsIntroduction. Preliminary Notes. Qualitative Properties Definitions. Lyapunov Functions and their Fractional Derivatives. Fractional Comparison Lemmas. Stability and Boundedness. Lyapunov Stability. Theorems on Boundedness. Global Stability. Mittag-Leffler Stability. Practical Stability. Lipschitz Stability. Stability of Sets. Stability of Integral Manifolds. Almost Periodicity. Almost Periodic Solutions. Lyapunov Method for Almost Periodic Solutions. Uncertain Fractional Differential Systems. Applications. Fractional Impulsive Neural Networks. Stability and Synchronization. Almost Periodic Solutions. The Uncertain Case. Fractional Impulsive Biological Models. Lasota-Wazewska Models. Lotka-Volterra Models. Kolmogorov-type Models. Fractional Impulsive Models in Economics.
£114.00
Taylor & Francis Inc Variational and Potential Methods in the Theory
Book SynopsisElastic plates form a class of very important mechanical structures that appear in a wide range of practical applications, from building bodies to microchip production. As the sophistication of industrial designs has increased, so has the demand for greater accuracy in analysis. This in turn has led modelers away from Kirchoff's classical theory for thin plates and toward increasingly refined models that yield not only the deflection of the middle section, but also account for transverse shear deformation. The improved performance of these models is achieved, however, at the expense of a much more complicated system of governing equations and boundary conditions.In this Monograph, the authors conduct a rigorous mathematical study of a number of boundary value problems for the system of partial differential equations that describe the equilibrium bending of an elastic plate with transverse shear deformation. Specifically, the authors explore the existence, uniqueness, and continuous dependence of the solution on the data. In each case, they give the variational formulation of the problems and discuss their solvability in Sobolev spaces. They then seek the solution in the form of plate potentials and reduce the problems to integral equations on the contour of the domain.This treatment covers an extensive range of problems and presents the variational method and the boundary integral equation method applied side-by-side. Readers will find that this feature of the book, along with its clear exposition, will lead to a firm and useful understanding of both the model and the methods.Trade Review"It is amazing that the authors have managed to cover so many fundamental boundary-value problems and present the variational method and the boundary integral equation method applied side-by-side in a single volume…This feature of the book will certainly strengthen understanding of both the model and the methods. The writing style is very clear, the book is self-contained and easy to read, and it should be extremely valuable to researchers interested in applied analysis and mathematical models in elasticity."-Proceedings of the Edinburgh Mathematical Society (2002, vol. 45) "This book will be useful for mathematicians, theoretical engineers, and all interested in mathematical modeling in elasticity."-European Mathematical Society Newsletter, No. 40 (June 2001)Table of ContentsIntroduction. Formulation of the Problems. Variational Formulation of the Dirichlet and Neumann Problems. Boundary Integral Equations for the Dirichlet and Neumann Problems. Transmission Boundary Value Problems. Plate Weakened by a Crack. Boundary Value Problems with Other Types of Boundary Conditions. Plate on a Generalized Elastic Foundation. Appendix.
£161.50
Apple Academic Press Inc. Economic-Mathematical Methods and Models under
Book SynopsisIn this book on mathematical programming, the postulate spacial-time certainty of economic process at uncertainty conditions in finite-dimensional vector space and the principle piecewise-linear homogeneity of economic process at uncertainty conditions in finite-dimensional vector space are first suggested. A special theory on constructing piecewise-linear economic-mathematical models was developed, and a criterion of multivariate prediction of economic process and their control at uncertainty conditions in a finite-dimensional vector space was suggested. A packet of numerical programs for computer simulation in constructing and multivariate prediction of economic state with the help of n-element piecewise-linear economic-mathematical models with regard to the uncertainty factors effect in m-dimensional vector space is also suggested.This book is intended for students of economic and administrative specialties as well as for research associates in the sphere of economic-mathematical methods, management, and banking.Table of ContentsBrief Information on Finite-Dimensional Vector Space and its Application in Economics. Bases of Piecewise-Linear Economic-Mathematical Models with Regard to Influence of Unaccounted Factors in Finite-Dimensional Vector Space. Piecewise Linear Economic-Mathematical Models with Regard to Unaccounted Factors Influence in Three-Dimensional Vector Space. Piecewise-Linear Economic-Mathematical Models with Regard to Unaccounted Factors Influence on a Plane. Bases of Software for Computer Simulation and Multivariant Prediction of Economic Even at Uncertainty Conditions on the Base of N-Component Piecewise-Linear Economic-Mathematical Models in M-Dimensional Vector Space. Index.
£114.00
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Panorama der Mathematik
Book Synopsis„Was ist Mathematik?” – auf diese Frage gibt dieses dicke Buch zahllose Antworten. Mathematik ist eben viel mehr als ein Schul- und Studienfach oder Rechnen: Es ist Teil der menschlichen Kultur, ein riesiges aktives Forschungsgebiet und ein nützlicher Werkzeugkasten. „Was ist Mathematik?” – statt einer einzelnen Antwort zeichnen die Autoren ein Panorama, bunt und vielfältig. Da geht es um Philosophie, Beweise, große und kleine Probleme, fundamentale Konzepte, Teilgebiete, Forschungspraxis, Anwendungen der Mathematik. Und um Geschichten aus der Geschichte. Das Buch richtet sich an alle, die wissen und darüber nachdenken wollen, was Mathematik ist, insbesondere auch an Studierende der Mathematik. Es begleitet eine Vorlesung, die an der Freien Universität Berlin jährlich vor allem für Lehramtsstudierende angeboten wird.Table of ContentsWas ist Mathematik?- Mathematische Forschung.- Beweise.- Formeln, Zeichnungen und Bilder.- Philosophie der Mathematik.- Primzahlen.- Zahlenbereiche.- Unendlichkeit.- Dimensionen.- Zufall – Wahrscheinlichkeiten – Statistik.- Funktionen.- Anwendungen.- Rechnen.- Algorithmen und Komplexität.- Mathematik in der Öffentlichkeit.
£27.05
The University of Chicago Press Inference and Representation A Study in Modeling
Book SynopsisTrade Review“Beautifully bringing together historical and contemporary research on representations in science with themes from aesthetics and the philosophy of art, Suárez’s book is an outstanding interdisciplinary contribution to the philosophy of science. It is essential reading for anyone interested in modeling practices, their connections with the arts, and what this insightful combination of science, art, and practice might bring to the epistemology of science.” -- Chiara Ambrosio, University College London“Suárez has been a leading voice in the philosophy of modeling for the last two decades. This book is a wonderfully clear and compelling presentation of his ‘inferentialist theory of representation.’ The book will be a central resource for advanced undergraduate and graduate students, and required reading for every philosopher of science.” -- Martin Kusch, University of Vienna“Suárez has written a brilliant account of the inferential conception of scientific representation, its historical roots, and its application to contemporary scientific modeling. What stands out is his deflationist approach toward metaphysics, the streamlined account in terms of representational force and inferential capacity, and the connection to the phenomenology of artistic perception. A magnificent work.” -- Bas C. van Fraassen, Princeton University“Inference and Representation makes a strong case for an inferential conception of scientific modeling. It argues that the effectiveness of a model lies in its providing an orientation that facilitates fruitful scientific reasoning. It is a valuable contribution to the literature on modeling.” -- Catherine Z. Elgin, Harvard University“This much-anticipated book is the culmination of over twenty years of pioneering work by Suárez. It is a must-read for anyone wishing to think carefully about models and representations in science. Suárez gives a careful, insightful, and comprehensive exposition and defence of his inferential conception of representation, and he now develops it in an expressly pragmatist direction with a helpful focus on the uses of models. What emerges is a compelling deflationary account of ‘representation without metaphysics,’ engaging fully with the complex realities of inferential practices. Suárez argues that common notions of representation based on similarity or isomorphism are ill-fitting and inadequate, and shows how the activity of representation pervades all sorts of scientific practices. His discussion is clear and systematic throughout, and successfully combines philosophical acuity and historical awareness. In the course of presenting his own position he also gives a fair, critical summing-up and evaluation of the considerable existing literature on models and representation. This landmark work should appeal to philosophers, historians of science and practicing scientists alike.” -- Hasok Chang, University of Cambridge“During the past quarter-century, philosophers of science have come to appreciate the importance of models and modeling practices in the sciences. Suárez has been one of the pioneers in this work, specifically in investigating how models represent aspects of the world. The present book is the culmination of insights accumulated over more than two decades. It provides a convincing account of representation, one emphasizing the uses to which models are put and the inferences they allow. Suárez develops his views with welcome precision, focuses on an admirably wide range of types of models, and offers numerous insights about the historical development of modeling. His final two chapters explore the notion of representation more broadly, with a lucid and well-informed discussion of representation in visual art, and draw out the implications for several large issues in the philosophy of science. This book is an outstanding contribution to the field.” -- Philip Kitcher, Columbia UniversityTable of ContentsPreface and Acknowledgments 1 Introducing Scientific Representation Part I Modeling 2 The Modeling Attitude: A Genealogy 3 Models and Their Uses Part II Representation 4 Theories of Representation 5 Against Substance 6 Scientific Theories and Deflationary Representation 7 Representation as Inference Part III Implications 8 Lessons from the Philosophy of Art 9 Scientific Epistemology Transformed Notes References Index
£84.00
Springer New York DiscreteEvent Simulation
a huge range and FREE tracked UK delivery on ALL orders.
£67.49
Springer New York Modeling Survival Data Extending the Cox Model Statistics for Biology and Health
a huge range and FREE tracked UK delivery on ALL orders.
£134.99
John Wiley & Sons Inc Mathematical Finance
Book SynopsisA balanced introduction to the theoretical foundations and real-world applications of mathematical finance The ever-growing use of derivative products makes it essential for financial industry practitioners to have a solid understanding of derivative pricing. To cope with the growing complexity, narrowing margins, and shortening life-cycle of the individual derivative product, an efficient, yet modular, implementation of the pricing algorithms is necessary. Mathematical Finance is the first book to harmonize the theory, modeling, and implementation of today''s most prevalent pricing models under one convenient cover. Building a bridge from academia to practice, this self-contained text applies theoretical concepts to real-world examples and introduces state-of-the-art, object-oriented programming techniques that equip the reader with the conceptual and illustrative tools needed to understand and develop successful derivative pricing models. Utilizing almost tweTrade Review"…very useful to practitioners and students…" (MAA Reviews, December 26, 2007) "An excellent textbook for students in mathematical finance, computational finance, and derivative pricing courses at the upper undergraduate or beginning graduate level." (Mathematical Reviews 2007)Table of Contents1. Introduction. 1.1 Theory, Modeling and Implementation. 1.2 Interest Rate Models and Interest Rate Derivatives. 1.3 How to Read this Book. 1.3.1 Abridged Versions. 1.3.2 Special Sections. 1.3.3 Notation. I: FOUNDATIONS. 2. Foundations. 2.1 Probability Theory. 2.2 Stochastic Processes. 2.3 Filtration. 2.4 Brownian Motion. 2.5 Wiener Measure, Canonical Setup. 2.6 Itô Calculus. 2.6.1 Itô Integral. 2.6.2 Itô Process. 2.6.3 Itô Lemma and Product Rule. 2.7 Brownian Motion with Instantaneous Correlation. 2.8 Martingales. 2.8.1 Martingale Representation Theorem. 2.9 Change of Measure (Girsanov, Cameron, Martin). 2.10 Stochastic Integration. 2.11 Partial Differential Equations (PDE). 2.11.1 Feynman-Kac Theorem . 2.12 List of Symbols. 3. Replication. 3.1 Replication Strategies. 3.1.1 Introduction. 3.1.2 Replication in a discrete Model. 3.2 Foundations: Equivalent Martingale Measure. 3.2.1 Challenge and Solution Outline. 3.2.2 Steps towards the Universal Pricing Theorem. 3.3 Excursus: Relative Prices and Risk Neutral Measures. 3.3.1 Why relative prices? 3.3.2 Risk Neutral Measure. II: FIRST APPLICATIONS. 4. Pricing of a European Stock Option under the Black-Scholes Model. 5. Excursus: The Density of the Underlying of a European Call Option. 6. Excursus: Interpolation of European Option Prices. 6.1 No-Arbitrage Conditions for Interpolated Prices. 6.2 Arbitrage Violations through Interpolation. 6.2.1 Example (1): Interpolation of four Prices. 6.2.2 Example (2): Interpolation of two Prices. 6.3 Arbitrage-Free Interpolation of European Option Prices. 7. Hedging in Continuous and Discrete Time and the Greeks. 7.1 Introduction. 7.2 Deriving the Replications Strategy from Pricing Theory. 7.2.1 Deriving the Replication Strategy under the Assumption of a Locally Riskless Product. 7.2.2 The Black-Scholes Differential Equation. 7.2.3 The Derivative V(t) as a Function of its Underlyings S i(t). 7.2.4 Example: Replication Portfolio and PDE under a Black-Scholes Model. 7.3 Greeks. 7.3.1 Greeks of a European Call-Option under the Black-Scholes model. 7.4 Hedging in Discrete Time: Delta and Delta-Gamma Hedging. 7.4.1 Delta Hedging. 7.4.2 Error Propagation. 7.4.3 Delta-Gamma Hedging. 7.4.4 Vega Hedging. 7.5 Hedging in Discrete Time: Minimizing the Residual Error (Bouchaud-Sornette Method). 7.5.1 Minimizing the Residual Error at Maturity T. 7.5.2 Minimizing the Residual Error in each Time Step. III: INTEREST RATE STRUCTURES, INTEREST RATE PRODUCTS AND ANALYTIC PRICING FORMULAS. Motivation and Overview. 8. Interest Rate Structures. 8.1 Introduction. 8.1.1 Fixing Times and Tenor Times. 8.2 Definitions. 8.3 Interest Rate Curve Bootstrapping. 8.4 Interpolation of Interest Rate Curves. 8.5 Implementation. 9. Simple Interest Rate Products. 9.1 Interest Rate Products Part 1: Products without Optionality. 9.1.1 Fix, Floating and Swap. 9.1.2 Money-Market Account. 9.2 Interest Rate Products Part 2: Simple Options. 9.2.1 Cap, Floor, Swaption. 9.2.2 Foreign Caplet, Quanto. 10. The Black Model for a Caplet. 11. Pricing of a Quanto Caplet (Modeling the FFX). 11.1 Choice of Numéraire. 12. Exotic Derivatives. 12.1 Prototypical Product Properties. 12.2 Interest Rate Products Part 3: Exotic Interest Rate Derivatives. 12.2.1 Structured Bond, Structured Swap, Zero Structure. 12.2.2 Bermudan Option. 12.2.3 Bermudan Callable and Bermudan Cancelable. 12.2.4 Compound Options. 12.2.5 Trigger Products. 12.2.6 Structured Coupons. 12.2.7 Shout Options. 12.3 Product Toolbox. IV: DISCRETIZATION AND NUMERICAL VALUATION METHODS. Motivation and Overview. 13. Discretization of time and state space. 13.1 Discretization of Time: The Euler and the Milstein Scheme. 13.1.1 Definitions. 13.1.2 Time-Discretization of a Lognormal Process. 13.2 Discretization of Paths (Monte-Carlo Simulation) . 13.2.1 Monte-Carlo Simulation. 13.2.2 Weighted Monte-Carlo Simulation. 13.2.3 Implementation. 13.2.4 Review. 13.3 Discretization of State Space. 13.3.1 Definitions. 13.3.2 Backward-Algorithm. 13.3.3 Review. 13.4 Path Simulation through a Lattice: Two Layers. 14. Numerical Methods for Partial Differential Equations. 15. Pricing Bermudan Options in a Monte Carlo Simulation. 15.1 Introduction. 15.2 Bermudan Options: Notation. 15.2.1 Bermudan Callable. 15.2.2 Relative Prices. 15.3 Bermudan Option as Optimal Exercise Problem. 15.3.1 Bermudan Option Value as single (unconditioned) Expectation: The Optimal Exercise Value. 15.4 Bermudan Option Pricing - The Backward Algorithm. 15.5 Re-simulation. 15.6 Perfect Foresight. 15.7 Conditional Expectation as Functional Dependence. 15.8 Binning. 15.8.1 Binning as a Least-Square Regression. 15.9 Foresight Bias. 15.10 Regression Methods - Least Square Monte-Carlo. 15.10.1 Least Square Approximation of the Conditional Expectation. 15.10.2 Example: Evaluation of a Bermudan Option on a Stock (Backward Algorithm with Conditional Expectation Estimator). 15.10.3 Example: Evaluation of a Bermudan Callable. 15.10.4 Implementation. 15.10.5 Binning as linear Least-Square Regression. 15.11 Optimization Methods. 15.11.1 Andersen Algorithm for Bermudan Swaptions. 15.11.2 Review of the Threshold Optimization Method. 15.11.3 Optimization of Exercise Strategy: A more general Formulation. 15.11.4 Comparison of Optimization Method and Regression. Method. 15.12 Duality Method: Upper Bound for Bermudan Option Prices. 15.12.1 Foundations. 15.12.2 American Option Evaluation as Optimal Stopping Problem. 15.13 Primal-Dual Method: Upper and Lower Bound. 16. Pricing Path-Dependent Options in a Backward Algorithm. 16.1 Evaluation of a Snowball / Memory in a Backward Algorithm. 16.2 Evaluation of a Flexi Cap in a Backward Algorithm. 17. Sensitivities (Partial Derivatives) of Monte Carlo Prices. 17.1 Introduction. 17.2 Problem Description. 17.2.1 Pricing using Monte-Carlo Simulation. 17.2.2 Sensitivities from Monte-Carlo Pricing. 17.2.3 Example: The Linear and the Discontinuous Payout. 17.2.4 Example: Trigger Products. 17.3 Generic Sensitivities: Bumping the Model. 17.4 Sensitivities by Finite Differences. 17.4.1 Example: Finite Differences applied to Smooth and Discontinuous Payout. 17.5 Sensitivities by Pathwise Differentiation. 17.5.1 Example: Delta of a European Option under a Black-Scholes Model. 17.5.2 Pathwise Differentiation for Discontinuous Payouts. 17.6 Sensitivities by Likelihood Ratio Weighting. 17.6.1 Example: Delta of a European Option under a Black-Scholes Model using Pathwise Derivative. 17.6.2 Example: Variance Increase of the Sensitivity when using Likelihood Ratio Method for Smooth Payouts. 17.7 Sensitivities by Malliavin Weighting. 17.8 Proxy Simulation Scheme. 18. Proxy Simulation Schemes for Monte Carlo Sensitivities and Importance Sampling. 18.1 Full Proxy Simulation Scheme. 18.1.1 Calculation of Monte-Carlo weights. 18.2 Sensitivities by Finite Differences on a Proxy Simulation Scheme. 18.2.1 Localization. 18.2.2 Object-Oriented Design. 18.3 Importance Sampling. 18.3.1 Example. 18.4 Partial Proxy Simulation Schemes. 18.4.1 Linear Proxy Constraint. 18.4.2 Comparison to Full Proxy Scheme Method. 18.4.3 Non-Linear Proxy Constraint. 18.4.4 Transition Probability from a Nonlinear Proxy Constraint. 18.4.5 Sensitivity with respect to the Diffusion Coefficients - Vega. 18.4.6 Example: LIBOR Target Redemption Note. 18.4.7 Example: CMS Target Redemption Note. V: PRICING MODELS FOR INTEREST RATE DERIVATIVES. 19. LIBOR Market Models. 19.1 LIBOR Market Model. 19.1.1 Derivation of the Drift Term. 19.1.2 The Short Period Bond P(Tm(t)+1;t) . 19.1.3 Discretization and (Monte-Carlo) Simulation. 19.1.4 Calibration - Choice of the free Parameters. 19.1.5 Interpolation of Forward Rates in the LIBOR Market Model. 19.2 Object Oriented Design. 19.2.1 Reuse of Implementation. 19.2.2 Separation of Product and Model. 19.2.3 Abstraction of Model Parameters. 19.2.4 Abstraction of Calibration. 19.3 Swap Rate Market Models (Jamshidian 1997). 19.3.1 The Swap Measure. 19.3.2 Derivation of the Drift Term. 19.3.3 Calibration - Choice of the free Parameters. 20. Swap Rate Market Models. 20.1 Definitions. 20.2 Terminal Correlation examined in a LIBOR Market Model Example. 20.2.1 De-correlation in a One-Factor Model. 20.2.2 Impact of the Time Structure of the Instantaneous Volatility on Caplet and Swaption Prices. 20.2.3 The Swaption Value as a Function of Forward Rates. 20.3 Terminal Correlation is dependent on the Equivalent Martingale Measure. 20.3.1 Dependence of the Terminal Density on the Martingale Measure. 21. Excursus: Instantaneous Correlation and Terminal Correlation. 21.1 Short Rate Process in the HJM Framework. 21.2 The HJM Drift Condition. 22.Heath-Jarrow-Morton Framework: Foundations. 22.1 Introduction. 22.2 The Market Price of Risk. 22.3 Overview: Some Common Models. 22.4 Implementations. 22.4.1 Monte-Carlo Implementation of Short-Rate Models. 22.4.2 Lattice Implementation of Short-Rate Models. 23. Short-Rate Models. 23.1 Short Rate Models in the HJM Framework. 23.1.1 Example: The Ho-Lee Model in the HJM Framework. 23.1.2 Example: The Hull-White Model in the HJM Framework. 23.2 LIBOR Market Model in the HJM Framework. 23.2.1 HJM Volatility Structure of the LIBOR Market Model. 23.2.2 LIBOR Market Model Drift under the QB Measure. 23.2.3 LIBOR Market Model as a Short Rate Model. 24 Heath-Jarrow-Morton Framwork: Immersion of Short-Rate Models and LIBOR Market Model. 24.1 Model. 24.2 Interpretation of the Figures. 24.3 Mean Reversion. 24.4 Factors. 24.5 Exponential Volatility Function. 24.6 Instantaneous Correlation. 25. Excursus: Shape of teh Interst Rate Curve under Mean Reversion and a Multifactor Model. 25.1 Introduction. 25.2 Cheyette Model. 26. Ritchken-Sakarasubramanian Framework: JHM with Low Markov Dimension. 26.1 Introduction. 26.1.1 The Markov Functional Assumption (independent of the model considered) . 26.1.2 Outline of this Chapter . 26.2 Equity Markov Functional Model. 26.2.1 Markov Functional Assumption. 26.2.2 Example: The Black-Scholes Model. 26.2.3 Numerical Calibration to a Full Two-Dimensional European Option Smile Surface. 26.2.4 Interest Rates. 26.2.5 Model Dynamics. 26.2.6 Implementation. 26.3 LIBOR Markov Functional Model. 26.3.1 LIBOR Markov Functional Model in Terminal Measure. 26.3.2 LIBOR Markov Functional Model in Spot Measure. 26.3.3 Remark on Implementation. 26.3.4 Change of numéraire in a Markov-Functional Model. 26.4 Implementation: Lattice. 26.4.1 Convolution with the Normal Probability Density. 26.4.2 State space discretization. Markov Functional Models. PART VI: Extended Models. 27.1 Introduction - Different Types of Spreads. 27.1.1 Spread on a Coupon. 27.1.2 Credit Spread. 27.2 Defaultable Bonds. 27.3 Integrating deterministic Credit Spread into a Pricing Model. 27.3.1 Deterministic Credit Spread. 27.3.2 Implementation. 27.4 Receiver’s and Payer’s Credit Spreads. 27.4.1 Example: Defaultable Forward Starting Coupon Bond. 27.4.2 Example: Option on a Defaultable Coupon Bond. 28. Credit Spreads. 28.1 Cross Currency LIBOR Market Model. 28.1.1 Derivation of the Drift Term under Spot-Measure. 28.1.2 Implementation. 28.2 Equity Hybrid LIBOR Market Model. 28.2.1 Derivation of the Drift Term under Spot-Measure. 28.2.2 Implementation. 28.3 Equity-Hybrid Cross-Currency LIBOR Market Model. 28.3.1 Summary. 28.3.2 Implementation. 29. Hybrid Models. 29.1 Elements of Object Oriented Programming: Class and Objects. 29.1.1 Example: Class of a Binomial Distributed Random Variable. 29.1.2 Constructor. 29.1.3 Methods: Getter, Setter, Static Methods. 29.2 Principles of Object Oriented Programming. 29.2.1 Encapsulation and Interfaces. 29.2.2 Abstraction and Inheritance. 29.2.3 Polymorphism. 29.3 Example: A Class Structure for One Dimensional Root Finders. 29.3.1 Root Finder for General Functions. 29.3.2 Root Finder for Functions with Analytic Derivative: Newton Method. 29.3.3 Root Finder for Functions with Derivative Estimation: Secant Method. 29.4 Anatomy of a Java™ Class. 29.5 Libraries. 29.5.1 Java™2 Platform, Standard Edition (j2se). 29.5.2 Java™2 Platform, Enterprise Edition (j2ee). 29.5.3 Colt. 29.5.4 Commons-Math: The Jakarta Mathematics Library. 29.6 Some Final Remarks. 29.6.1 Object Oriented Design (OOD) / Unified Modeling Language. PART VII: Implementation 30. Object-Oriented Implementatin in JavaTM. PART VIII: Appendices. A: A small Collection of Common Misconceptions. B: Tools (Selection). B.1 Linear Regression. B.2 Generation of Random Numbers. B.2.1 Uniform Distributed Random Variables. B.2.2 Transformation of the Random Number Distribution via the Inverse Distribution Function. B.2.3 Normal Distributed Random Variables. B.2.4 Poisson Distributed Random Variables. B.2.5 Generation of Paths of an n-dimensional Brownian Motion. B.3 Factor Decomposition - Generation of Correlated Brownian Motion. B.4 Factor Reduction. B.5 Optimization (one-dimensional): Golden Section Search. B.6 Convolution with Normal Density. C: Exercises. D: List of Symbols. E: Java™ Source Code (Selection). E.1 Java™ Classes for Chapter 29. List of Figures. List of Tables. List of Listings. Bibliography. Index.
£129.56
John Wiley & Sons Inc Generalized Linear and Mixed Models
a huge range and FREE tracked UK delivery on ALL orders.
£143.06
John Wiley & Sons Inc Cellular Automata
Book SynopsisAn accessible and multidisciplinaryintroduction to cellular automata As the applicability of cellular automata broadens and technology advances, there is a need for a concise, yet thorough, resource that lays the foundation of key cellularautomata rules and applications. In recent years, Stephen Wolfram''s A New Kind of Science has brought the modeling power that lies in cellular automata to the attentionof the scientific world, and now, Cellular Automata: A Discrete View of the World presents all the depth, analysis, and applicability of the classic Wolfram text in a straightforward, introductory manner. This book offers an introduction to cellular automata as a constructive method for modeling complex systems where patterns of self-organization arising from simple rules are revealed in phenomena that exist across a wide array of subject areas, including mathematics, physics, economics, and the social sciences. The book begins with a preliminary introduction to cellular autTrade Review"The book is well produced and a good introduction to its subject." (Computing Reviews, January 30, 2009) "An interesting read and worth browsing by somebody interested in getting a general background on CA. The examples are many and varied, and the numerous citations--both to electronic and printed media--are very helpful." (Computing Reviews, November 11, 2008) "An interesting read and worth browsing by somebody interested in getting a general background on CA. The examples are many and varied, and the numerous citations--both to electronic and printed media--are very helpful." (Computing Reviews, Nov 2008) "Schiff suppresses most mathematical details, rendering his book highly accessible, informative, and entertaining, but leaving open niches for a textbook treatment with exercises or an advanced monograph with proofs." (CHOICE, October 2008) "This book serves as a valuable resource for undergraduate and graduate students in the physical, biological, and social sciences and may also be of interest to any reader with a scientific or basic mathematical ground." (Mathematical Reviews, 2008m) "Schiff suppresses most mathematical details, rendering his book highly accessible, informative, and entertaining, but leaving open niches for a textbook treatment with exercises or an advanced monograph with proofs." (CHOICE Oct 2008) "This book serves as a valuable resource for undergraduate and graduate students in the physical, biological, and social sciences and may also be of interest to any reader with a scientific or basic mathematical ground." (Mathematical Reviews 2008)Table of ContentsPreface. 1. Preliminaries. 2. Dynamical Systems. 3. One-Dimensional Cellular Automata. 4. Two-Dimensional Automata. 5. Applications. 6. Complexity. Appendix A. References. Index.
£125.96
John Wiley & Sons Inc Modeling and Simulation for Analyzing Global
Book Synopsisone-of-a-kind introduction to the theory and application of modeling and simulation techniques in the realm of international studies Modeling and Simulation for Analyzing Global Events provides an orientation to the theory and application of modeling and simulation techniques in social science disciplines.Table of ContentsPreface. I PRINCIPLES OF MODELING AND SIMULATION: ADVANCING GLOBAL STUDIES. 1 Modeling and Simulation: What, When, and Why. Introduction. An Overview of Modeling and Simulation. A Brief History of Modeling and Simulation. Why Use Modeling and Simulation. Conclusions. Key Terms. References. Further Reading. 2 Research Methodologies for Modeling Global Events. Introduction. Global Events and the Social Sciences. Qualitative and Quantitative Research. Modeling and Simulation of Global Events. Mapping Data: A Suggested Methodology. Model Validation. Conclusions. Key Terms. References. II MODELING PARADIGMS. 3 System Dynamics. Introduction. Dynamic System Behavior. Building Blocks of System Dynamics Models. Conclusions. Key Terms. References. 4 Agent-Based Modeling and Social Networks. Introduction. Agent-Based Models: Description and Definition. Social Networks. Building an Agent-Based Model. Conclusions. Key Terms. References. 5 Game Theory. Introduction. Fundamentals of Game Theory. Types of Games. Conclusions. Key Terms. References. III MODELING GLOBAL EVENTS. 6 Case Study: Colombia—A Country Study on Insurgency. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Mapping Qualitative to Quantitative. System Dynamics. Responding to the Research Question. Key Terms. References. Case Study Bibliography. 7 Case Study: The Polish Solidarity Movement—Laying the Foundation for the Collapse of Soviet Communism. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Measuring Agents and Environments: Stimuli and Actions. Modeling Human Behavior with Agents. Responding to the Research Question. Conclusions. Key Terms. References. Case Study Bibliography. 8 Case Study: Vietnam—Johnson’s War, 1963–1965. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Analyzing the Social Network Structures. Social Network Aspects of Human Behavior Modeling. Agent-Based Model Development. Responding to the Research Question. Key Terms. References. Case Study Bibliography. 9 Case Study: Cuban Missile Crisis—A National Security Emergency. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Evaluating Behaviors. Game Theory. Responding to the Research Question. Key Terms. References. Case Study Bibliography. Index.
£95.36
John Wiley & Sons Inc Handbook in Monte Carlo Simulation
Book SynopsisAn accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The Handbook in Monte Carlo Simulation features: An introductorTable of ContentsPreface xiii Part I Overview and Motivation 1 Introduction to Monte Carlo Methods 3 1.1 Historical origin of Monte Carlo simulation 4 1.2 Monte Carlo Simulation vs. Monte Carlo Sampling 7 1.3 System dynamics and the mechanics of Monte Carlo simulation 10 1.4 Simulation and optimization 21 1.5 Pitfalls in Monte Carlo simulation 30 1.6 Software tools for Monte Carlo simulation 35 1.7 Prerequisites 37 For further reading 38 Chapter References 38 2 Numerical Integration Methods 41 2.1 Classical quadrature formulae 43 2.2 Gaussian quadrature 48 2.3 Extension to higher dimensions: Product rules 53 2.4 Alternative approaches for high-dimensional integration 55 2.5 Relationship with moment matching 67 2.6 Numerical integration in R 69 For further reading 71 Chapter References 71 Part II Input Analysis: Modeling and Estimation 3 Stochastic Modeling in Finance and Economics 75 3.1 Introductory examples 77 3.2 Some common probability distributions 86 3.3 Multivariate distributions: Covariance and correlation 111 3.4 Modeling dependence with copulae 127 3.5 Linear regression models: a probabilistic view 136 3.6 Time series models 137 3.7 Stochastic differential equations 158 3.8 Dimensionality reduction 177 S3.1 Risk-neutral derivative pricing 190 S3.1.1 Option pricing in the binomial model 192 S3.1.2 A continuous-time model for option pricing: The Black–Scholes–Merton formula 194 S3.1.3 Option pricing in incomplete markets 199 For further reading 202 Chapter References 203 4 Estimation and Fitting 205 4.1 Basic inferential statistics in R 207 4.2 Parameter estimation 215 4.3 Checking the fit of hypothetical distributions 224 4.4 Estimation of linear regression models by ordinary least squares 229 4.5 Fitting time series models 232 4.6 Subjective probability: the Bayesian view 235 For further reading 244 Chapter References 245 Part III Sampling and Path Generation 5 Random Variate Generation 249 5.1 The structure of a Monte Carlo simulation 250 5.2 Generating pseudo-random numbers 252 5.3 The inverse transform method 263 5.4 The acceptance–rejection method 265 5.5 Generating normal variates 269 5.6 Other ad hoc methods 274 5.7 Sampling from copulae 276 For further reading 277 Chapter References 279 6 Sample Path Generation for Continuous-Time Models 281 6.1 Issues in path generation 282 6.2 Simulating geometric Brownian motion 287 6.3 Sample paths of short-term interest rates 298 6.4 Dealing with stochastic volatility 306 6.5 Dealing with jumps 308 For further reading 310 Chapter References 311 Part IV Output Analysis and Efficiency Improvement 7 Output Analysis 315 7.1 Pitfalls in output analysis 317 7.2 Setting the number of replications 323 7.3 A world beyond averages 325 7.4 Good and bad news 327 For further reading 327 Chapter References 328 8 Variance Reduction Methods 329 8.1 Antithetic sampling 330 8.2 Common random numbers 336 8.3 Control variates 337 8.4 Conditional Monte Carlo 341 8.5 Stratified sampling 344 8.6 Importance sampling 350 For further reading 363 Chapter References 363 9 Low-Discrepancy Sequences 365 9.1 Low-discrepancy sequences 366 9.2 Halton sequences 367 9.3 Sobol low-discrepancy sequences 374 9.4 Randomized and scrambled low-discrepancy sequences 379 9.5 Sample path generation with low-discrepancy sequences 381 For further reading 385 Chapter References 385 Part V Miscellaneous Applications 10 Optimization 389 10.1 Classification of optimization problems 390 10.2 Optimization model building 405 10.3 Monte Carlo methods for global optimization 412 10.4 Direct search and simulation-based optimization methods 416 10.5 Stochastic programming models 420 10.6 Scenario generation and Monte Carlo methods for stochastic programming 428 10.7 Stochastic dynamic programming 433 10.8 Numerical dynamic programming 440 10.9 Approximate dynamic programming 451 For further reading 453 Chapter References 453 11 Option Pricing 455 11.1 European-style multidimensional options in the BSM world 456 11.2 European-style path-dependent options in the BSM world 462 11.3 Pricing options with early exercise features 475 11.4 A look outside the BSM world 487 11.5 Pricing interest-rate derivatives 490 For further reading 497 Chapter References 498 12 Sensitivity Estimation 501 12.1 Estimating option greeks by finite differences 503 12.2 Estimating option greeks by pathwise derivatives 509 12.3 Estimating option greeks by the likelihood ratio method 513 For further reading 517 Chapter References 518 13 Risk Measurement and Management 519 13.1 What is a risk measure? 520 13.2 Quantile-based risk measures: value at risk 522 13.3 Monte Carlo methods for V@R 533 13.4 Mean-risk models in stochastic programming 537 13.5 Simulating delta-hedging strategies 540 13.6 The interplay of financial and nonfinancial risks 546 For further reading 548 Chapter References 548 14 Markov Chain Monte Carlo and Bayesian Statistics 551 14.1 An introduction to Markov chains 552 14.2 The Metropolis–Hastings algorithm 555 14.3 A re-examination of simulated annealing 558 For further reading 560 Chapter References 561 Index 563
£116.06
John Wiley & Sons Inc Graphical Models in Applied Multivariate
Book Synopsis- It reveals the interrelationships between multiple variables and features of the underlying conditional independence. - It covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. - Many numerical examples and exercises with solutions are included.Table of ContentsIndependence and Interaction. Independence Graphs. Information Divergence. The Inverse Variance. Graphical Gaussian Models. Graphical Log-Linear Models. Model Selection. Methods for Sparse Tables. Regression and Graphical Chain Models. Models for Mixed Variables. Decompositions and Decomposability. Appendices. References. Author Index. Subject Index.
£63.60
John Wiley & Sons Inc Modeling and Simulation in the Medical and Health
Book SynopsisDetailing the link between computational models and physical models, Modeling and Simulation in the Medical and Health Sciences encourages a more uniform discussion of simulation within both the engineering and medical domains.Table of ContentsContributors. Foreword. Preface. Part One Fundamentals of Medical and Health Sciences Modeling and Simulation. 1 Introduction to Modeling and Simulation in the Medical and Health Sciences (Catherine M. Banks). 2 The Practice of Modeling and Simulation: Tools of the Trade (John A. Sokolowski). Part Two. Modeling for the Medical and Health Sciences. 3 Mathematical Models of Tumor Growth and Wound Healing (John A. Adam). 4 Physical Modeling (Stacie I. Ringleb). Part Three. Modeling and Simulation Applications. 5 Humans as Models (C. Donald Combs). 6 Modeling the Human System (Mohammed Ferdjallah and Gyu Tae Kim). 7 Robotics (Richard Lee). 8 Training (Paul E. Phrampus). 9 Patient Care (Eugene Santos Jr, Joseph Rosen, Keum Joo Kim, Fei Yu, Dequing Li, Elizabeth Jacob, Lindsay Katona). 10 Future of Modeling and Simulation in the Medical and Health Sciences (Richard M. Satava). Appendix. Index.
£76.46
John Wiley & Sons Inc Groundwater Hydrology
Book SynopsisGroundwater is a vital source of water throughout the world. As the number of groundwater investigations increase, it is important to understand how to develop comprehensive quantified conceptual models and appreciate the basis of analytical solutions or numerical methods of modelling groundwater flow. Groundwater Hydrology: Conceptual and Computational Models describes advances in both conceptual and numerical modelling. It gives insights into the interpretation of field information, the development of conceptual models, the use of computational models based on analytical and numerical techniques, the assessment of the adequacy of models, and the use of computational models for predictive purposes. It focuses on the study of groundwater flow problems and a thorough analysis of real practical field case studies. It is divided into three parts: * Part I deals with the basic principles, including a summary of mathematical descriptions of groundwater flow, recharge estimTrade Review"...well written and structured...a comprehensive and thorough reference source...highly recommended for anyone in the business..." (Circulation - N'ltr of British Hydrological Soc, Feb 2004) "...delighted to have this book on my shelf and it is already becoming well thumbed...no hesitation in recommending it..." (Geoscientist, May 2004) "The information and techniques presented in this book provide illuminating guidelines and application directions for practicing hydrogeologists, geohydrologists and water resource engineers." (Hydrological Sciences Journal, Feb 2005, Vol 50 (1))Table of ContentsPreface. 1. Introduction. PART I: BASIC PRINCIPLES. 2. Background to Groundwater Flow. 3. Recharge due to Precipitation or Irrigation. 4. Interaction between Surface Water and Groundwater. PART II: RADIAL FLOW. 5. Radial Flow to Pumped Boreholes – Fundamental Issues. 6. Large Diameter Wells. 7. Radial Flow where Vertical Components of Flow are Significant. 8. Practical Issues of Interpretation and Assessing Resources. PART III: REGIONAL GROUNDWATER FLOW. 9. Regional Groundwater Studies in which Transmissivity is Effectively Constant. 10. Regional Groundwater Flow in Multi-Aquifer Systems. 11. Regional Groundwater Flow with Hydraulic Conductivity Varying with Saturated Thickness. 12. Numerical Modelling Insights. Appendix: Computer Program for Two-zone Model. List of Symbols. References. Index.
£127.76
John Wiley & Sons Inc Engineering Principles of Combat Modeling and
Book SynopsisThis book covers engineering principles and state-of-the-art methods involved in the many facets of combat modeling and distributed simulation.Trade Review“Tolk and his coauthors have extensive experience in this area, making this volume a standard reference for researchers engaged in combat modeling. The complexity of the domain, the consequences of error, and the prohibitive cost of direct experimentation are as great in combat modeling as in any other problem area, making this volume a valuable source of examples and techniques for modelers in other areas that are highly complex, consequential, and inaccessible by direct experiment." (Computing Reviews, 1 October 2012) Table of ContentsPreface xi Contributors xiii Biographies xvii Acknowledgments xxvii Abbreviations xxix 1. Challenges of Combat Modeling and Distributed Simulation 1 Andreas Tolk Part I Foundations 2. Applicable Codes of Ethics 25 Andreas Tolk 3. The NATO Code of Best Practice for Command and Control Assessment 33 Andreas Tolk 4. Terms and Application Domains 55 Andreas Tolk 5. Scenario Elements 79 Andreas Tolk Part II Combat Modeling 6. Modeling the Environment 95 Andreas Tolk 7. Modeling Movement 113 Andreas Tolk 8. Modeling Sensing 127 Andreas Tolk 9. Modeling Effects 145 Andreas Tolk 10. Modeling Communications, Command, and Control 171 Andreas Tolk Part III Distributed Simulation 11. Challenges of Distributed Simulation 187 Andreas Tolk 12. Standards for Distributed Simulation 209 Andreas Tolk 13. Modeling and Simulation Development and Preparation Processes 243 Andreas Tolk 14. Verification and Validation 263 Andreas Tolk 15. Integration of M&S Solutions into the Operational Environment 295 Andreas Tolk Part IV Advanced Topics 16. History of Combat Modeling and Distributed Simulation 331 Margaret L. Loper and Charles Turnitsa 17. Serious Games, Virtual Worlds, and Interactive Digital Worlds 357 Roger D. Smith 18. Mathematical Applications for Combat Modeling 385 Patrick T. Hester and Andrew Collins 19. Combat Modeling with the High Level Architecture and Base Object Models 413 Mikel D. Petty and Paul Gustavson 20. The Test and Training Enabling Architecture (TENA) 449 Edward T. Powell and J. Russell Noseworthy 21. Combat Modeling using the DEVS Formalism 479 Tag Gon Kim and Il-Chul Moon 22. GIS Data for Combat Modeling 511 David Lashlee, Joe Bricio, Robert Holcomb, and William T. Richards 23. Modeling Tactical Data Links 537 Joe Sorroche 24. Standards-Based Combat Simulation Initialization using the Military Scenario Definition Language (MSDL) 579 Robert L. Wittman Jr 25. Multi-Resolution Combat Modeling 607 Mikel D. Petty, Robert W. Franceschini, and James Panagos 26. New Challenges: Human, Social, Cultural, and Behavioral Modeling 641 S. K. Numrich and P. M. Picucci 27. Agent Directed Simulation for Combat Modeling and Distributed Simulation 669 Gnana K. Bharathy, Levent Yilmaz, and Andreas Tolk 28. Uncertainty Representation and Reasoning for Combat Models 715 Paulo C. G. Costa, Heber Herencia-Zapana, and Kathryn Laskey 29. Model-Based Data Engineering for Distributed Simulations 747 Saikou Y. Diallo 30. Federated Simulation for System of Systems Engineering 765 Robert H. Kewley and Marc Wood 31. The Role of Architecture Frameworks in Simulation Models: The Human View Approach 811 Holly A. H. Handley 32. Multinational Computer Assisted Exercises 825 Erdal Cayirci Annex 1: M&S Organizations/Associations 841 Salim Chemlal and Tuncer Ören Annex 2: Military Simulation Systems 851 José J. Padilla Index 869
£118.76
John Wiley & Sons Inc Understanding and Managing Model Risk
Book SynopsisA guide to the validation and risk management of quantitative models used for pricing and hedging Whereas the majority of quantitative finance books focus on mathematics and risk management books focus on regulatory aspects, this book addresses the elements missed by this literature--the risks of the models themselves. This book starts from regulatory issues, but translates them into practical suggestions to reduce the likelihood of model losses, basing model risk and validation on market experience and on a wide range of real-world examples, with a high level of detail and precise operative indications.Table of ContentsPreface xi Acknowledgements xix Part I Theory and Practice of Model Risk Management 1 Understanding Model Risk 3 1.1 What Is Model Risk? 3 1.1.1 The Value Approach 4 1.1.2 The Price Approach 6 1.1.3 A Quant Story of the Crisis 9 1.1.4 A Synthetic View on Model Risk 17 1.2 Foundations of Modelling and the Reality of Markets 22 1.2.1 The Classic Framework 22 1.2.2 Uncertainty and Illiquidity 30 1.3 Accounting for Modellers 38 1.3.1 Fair Value 38 1.3.2 The Liquidity Bubble and the Accountancy Boards 40 1.3.3 Level 1, 2, 3 .go? 41 1.3.4 The Hidden Model Assumptions in ‘vanilla’ Derivatives 42 1.4 What Regulators Said After the Crisis 48 1.4.1 Basel New Principles: The Management Process 49 1.4.2 Basel New Principles: The Model, The Market and The Product 51 1.4.3 Basel New Principles: Operative Recommendations 52 1.5 Model Validation and Risk Management: Practical Steps 53 1.5.1 A Scheme for Model Validation 54 1.5.2 Special Points in Model Risk Management 59 1.5.3 The Importance of Understanding Models 60 2 Model Validation and Model Comparison: Case Studies 63 2.1 The Practical Steps of Model Comparison 63 2.2 First Example: The Models 65 2.2.1 The Credit Default Swap 66 2.2.2 Structural First-Passage Models 67 2.2.3 Reduced-Form Intensity Models 69 2.2.4 Structural vs Intensity: Information 72 2.3 First Example: The Payoff. Gap Risk in a Leveraged Note 74 2.4 The Initial Assessment 77 2.4.1 First Test: Calibration to Liquid Relevant Products 77 2.4.2 Second Test: a Minimum Level of Realism 78 2.5 The Core Risk in the Product 81 2.5.1 Structural Models: Negligible Gap Risk 82 2.5.2 Reduced-Form Models: Maximum Gap Risk 82 2.6 A Deeper Analysis: Market Consensus and Historical Evidence 85 2.6.1 What to Add to the Calibration Set 85 2.6.2 Performing Market Intelligence 86 2.6.3 The Lion and the Turtle. Incompleteness in Practice 86 2.6.4 Reality Check: Historical Evidence and Lack of it 87 2.7 Building a Parametric Family of Models 88 2.7.1 Understanding Model Implications 93 2.8 Managing Model Uncertainty: Reserves, Limits, Revisions 95 2.9 Model Comparison: Examples from Equity and Rates 99 2.9.1 Comparing Local and Stochastic Volatility Models in Pricing Equity Compound and Barrier Options 99 2.9.2 Comparing Short Rate and Market Models in Pricing Interest Rate Bermudan Options 105 3 Stress Testing and the Mistakes of the Crisis 111 3.1 Learning Stress Test from the Crisis 111 3.1.1 The Meaning of Stress Testing 112 3.1.2 Portfolio Stress Testing 113 3.1.3 Model Stress Testing 116 3.2 The Credit Market and the ‘Formula that Killed Wall Street’ 118 3.2.1 The CDO Payoff 118 3.2.2 The Copula 119 3.2.3 Applying the Copula to CDOs 122 3.2.4 The Market Quotation Standard 124 3.3 Portfolio Stress Testing and the Correlation Mistake 125 3.3.1 From Flat Correlation Towards a Realistic Approach 126 3.3.2 A Correlation Parameterization to Stress the Market Skew 131 3.4 Payoff Stress and the Liquidity Mistake 136 3.4.1 Detecting the Problem: Losses Concentrated in Time 137 3.4.2 The Problem in Practice 139 3.4.3 A Solution. From Copulas to Real Models 145 3.4.4 Conclusions 150 3.5 Testing with Historical Scenarios and the Concentration Mistake 151 3.5.1 The Mapping Methods for Bespoke Portfolios 152 3.5.2 The Lehman Test 156 3.5.3 Historical Scenarios to Test Mapping Methods 157 3.5.4 The Limits of Mapping and the Management of Model Risk 164 3.5.5 Conclusions 168 4 Preparing for Model Change. Rates and Funding in the New Era 171 4.1 Explaining the Puzzle in the Interest Rates Market and Models 171 4.1.1 The Death of a Market Model: 9 August 2007 173 4.1.2 Finding the New Market Model 174 4.1.3 The Classic Risk-free Market Model 178 4.1.4 A Market Model with Stable Default Risk 182 4.1.5 A Market with Volatile Credit Risk 192 4.1.6 Conclusions 200 4.2 Rethinking the Value of Money: The Effect of Liquidity in Pricing 201 4.2.1 The Setting 204 4.2.2 Standard DVA: Is Something Missing? 206 4.2.3 Standard DVA plus Liquidity: Is Something Duplicated? 207 4.2.4 Solving the Puzzle 207 4.2.5 Risky Funding for the Borrower 208 4.2.6 Risky Funding for the Lender and the Conditions for Market Agreement 209 4.2.7 Positive Recovery Extension 210 4.2.8 Two Ways of Looking at the Problem: Default Risk or Funding Benefit? The Accountant vs the Salesman 211 4.2.9 Which Direction for Future Pricing? 214 Part II Snakes in the Grass: Where Model Risk Hides 5 Hedging 219 5.1 Model Risk and Hedging 219 5.2 Hedging and Model Validation: What is Explained by P&L Explain? 221 5.2.1 The Sceptical View 222 5.2.2 The Fundamentalist View and Black and Scholes 222 5.2.3 Back to Reality 224 5.2.4 Remarks: Recalibration, Hedges and Model Instability 226 5.2.5 Conclusions: from Black and Scholes to Real Hedging 228 5.3 From Theory to Practice: Real Hedging 229 5.3.1 Stochastic Volatility Models: SABR 231 5.3.2 Test Hedging Behaviour Leaving Nothing Out 232 5.3.3 Real Hedging for Local Volatility Models 238 5.3.4 Conclusions: the Reality of Hedging Strategies 241 6 Approximations 243 6.1 Validate and Monitor the Risk of Approximations 243 6.2 The Swaption Approximation in the Libor Market Model 245 6.2.1 The Three Technical Problems in Interest Rate Modelling 245 6.2.2 The Libor Market Model and the Swaption Market 247 6.2.3 Pricing Swaptions 250 6.2.4 Understanding and Deriving the Approximation 253 6.2.5 Testing the Approximation 257 6.3 Approximations for CMS and the Shape of the Term Structure 264 6.3.1 The CMS Payoff 265 6.3.2 Understanding Convexity Adjustments 266 6.3.3 The Market Approximation for Convexity Adjustments 267 6.3.4 A General LMM Approximation 269 6.3.5 Comparing and Testing the Approximations 271 6.4 Testing Approximations Against Exact. Dupire’s Idea 276 6.4.1 Perfect Positive Correlation 278 6.4.2 Perfect Negative Correlation 280 6.5 Exercises on Risk in Computational Methods 283 6.5.1 Approximation 283 6.5.2 Integration 285 6.5.3 Monte Carlo 285 7 Extrapolations 287 7.1 Using the Market to Complete Information: Asymptotic Smile 288 7.1.1 The Indetermination in the Asymptotic Smile 288 7.1.2 Pricing CMS with a Smile: Extrapolating to Infinity 292 7.1.3 Using CMS Information to Transform Extrapolation into Interpolation and Fix the Indetermination 293 7.2 Using Mathematics to Complete Information: Correlation Skew 295 7.2.1 The Expected Tranched Loss 295 7.2.2 Properties for Interpolation 298 7.2.3 Properties for Turning Extrapolation into Interpolation 298 8 Correlations 303 8.1 The Technical Difficulties in Computing Correlations 303 8.1.1 Correlations in Interest Rate Modelling 305 8.1.2 Cross-currency Correlations 307 8.1.3 Stochastic Volatility Correlations 312 8.2 Fundamental Errors in Modelling Correlations 315 8.2.1 The Zero-correlation Error 316 8.2.2 The 1-Correlation Error 319 9 Calibration 323 9.1 Calibrating to Caps/Swaptions and Pricing Bermudans 324 9.1.1 Calibrating Caplets 325 9.1.2 Understanding the Term Structure of Volatility 326 9.1.3 Different Parameterizations 329 9.1.4 The Evolution of the Term Structure of Volatility 332 9.1.5 The Effect on Early-Exercise Derivatives 334 9.1.6 Reducing Our Indetermination in Pricing Bermudans: Liquid European Swaptions 335 9.2 The Evolution of the Forward Smiles 340 10 When the Payoff is Wrong 347 10.1 The Link Between Model Errors and Payoff Errors 347 10.2 The Right Payoff at Default: The Impact of the Closeout Convention 348 10.2.1 How Much Will be Paid at Closeout, Really? 350 10.2.2 What the Market Says and What the ISDA Says 352 10.2.3 A Quantitative Analysis of the Closeout 353 10.2.4 A Summary of the Findings and Some Conclusions on Payoff Uncertainty 360 10.3 Mathematical Errors in the Payoff of Index Options 362 10.3.1 Too Much Left Out 364 10.3.2 Too Much Left In 365 10.3.3 Empirical Results with the Armageddon Formula 365 10.3.4 Payoff Errors and Armageddon Probability 367 11 Model Arbitrage 371 11.1 Introduction 371 11.2 Capital Structure Arbitrage 373 11.2.1 The Credit Model 373 11.2.2 The Equity Model 375 11.2.3 From Barrier Options to Equity Pricing 377 11.2.4 Capital-structure Arbitrage and Uncertainty 381 11.3 The Cap-Swaption Arbitrage 391 11.4 Conclusion: Can We Use No-Arbitrage Models to Make Arbitrage? 394 12 Appendix 397 12.1 Random Variables 397 12.1.1 Generating Variables from Uniform Draws 397 12.1.2 Copulas 397 12.1.3 Normal and Lognormal 398 12.2 Stochastic Processes 399 12.2.1 The Law of Iterated Expectation 399 12.2.2 Diffusions, Brownian Motions and Martingales 400 12.2.3 Poisson Process 403 12.2.4 Time-dependent Intensity 404 12.3 Useful Results from Quantitative Finance 405 12.3.1 Black and Scholes (1973) and Black (1976) 405 12.3.2 Change of Numeraire 407 Bibliography 409 Index 417
£63.65
John Wiley & Sons Inc Environmental Modeling
Book SynopsisA comprehensive, thoroughly modern approach to environmental quality assessment The only textbook to combine engineering transport fundamentals and equilibrium aquatic chemistry, Environmental Modeling brings a uniquely contemporary perspective to the assessment of environmental quality. Addressing key questions about fate, transport, and long-term effects of chemical pollutants in the environment, this inherently practical text gives readers the important tools they need to develop and solve their own mathematical models. Contains detailed examples from a wide range of crucial water quality areas-conventional pollutants in rivers, eutrophication of lakes, and toxic organic chemicals and heavy metals in both surface and groundwaters Examines current global issues, including atmospheric deposition, hazardous wastes, soil pollution, global change, and more Features over 200 high-quality illustrations, plus skill-building problems in every chapter <Table of ContentsTransport Phenomena. Chemical Reaction Kinetics. Equilibrium Chemical Modeling. Eutrophication of Lakes. Conventional Pollutants in Rivers. Toxic Organic Chemicals. Modeling Trace Metals. Groundwater Contamination. Atmospheric Deposition and Biogeochemistry. Global Change and Global Cycles. Appendices. Index.
£155.66
John Wiley & Sons Inc Linear Models
Book SynopsisThis 1971 classic on linear models features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.Table of ContentsGeneralized Inverse Matrices. Distributions and Quadratic Forms. Regression, or the Full Rank Model. Introducing Linear Models: Regression on Dummy Variables. Models Not of Full Rank. Two Elementary Models. The 2-Way Crossed Classification. Some Other Analyses. Introduction to Variance Components. Methods of Estimating Variance Components from UnbalancedData. Variance Component Estimation from Unbalanced Data: Formulae. Literature Cited. Statistical Tables. Index.
£124.15
John Wiley & Sons Inc Urban Stormwater wWS
Book SynopsisUnderstanding how to properly manage urban stormwater is a critical concern to civil and environmental engineers the world over. Mismanagement of stormwater and urban runoff results in flooding, erosion, and water quality problems.Table of ContentsURBAN STORMWATER MANAGEMENT. Urban Drainage Systems: Evolution of Problems. Urban Runoff Quantity and Quality Control Strategies. Urban Stormwater Management Modeling. DATA ANALYSIS. Meteorological Data Analysis. Runoff Quality Data Analysis. DRAINAGE SYSTEM PERFORMANCE ANALYSIS. Elements of Derived Probability Distribution Theory. Model of Urban Drainage System. Quantity Control Analysis of Urban Drainage Systems. Advanced Quantity Control Analysis. Multiple Reservoir Systems. Quality Control Analysis of Urban Drainage Systems. Urban Drainage Systems Analysis: Optimization and SensitivityAnalysis. Appendices. Glossary. Notation. Index.
£124.15
John Wiley & Sons Inc New Directions in Mathematical Finance
Book SynopsisBased around a conference on financial modeling held in Milan in December 1999, this book brings together the leading names in quantitative finance to discuss the modeling techniques in a variety of areas of financial engineering.Table of ContentsPreface The Quantitative Finance Timeline (Paul Wilmott) Part I. New Directions in Equity Modelling Introduction Asymptotic analysis of stochastic volatility models (Henrik Rasmussen and Paul Wilmott) Passport options, a review (Antony Penaud) Equity Dividend Models (David Bakstein and Paul Wilmott) Isoperimetry, log-concavity and elasticity of option prices (Christer Borell) Part II. New Directions in Interest Rate Modelling Introduction Dynamic, deterministic and static optimal portfolio strategies in a mean-variance framework under stochastic interest rates (Isabelle Bajeux-Besnainou and Roland Portrait) Pricing bond options in a worst-case scenario (David Epstein and Paul Wilmott) Part III. New Directions in Risk Management Introduction Implementing VaR by Historical Simulation (Aldo Nassigh, Andrea Piazzetta and Ferdinando Samaria) CrashMetrics (Philip Hua and Paul Wilmott) Herding in financial markets: a role for psychology in explaining investor behaviour? (Henriëtte Prast) Further Reading Author Biographies Index
£95.00
John Wiley & Sons Inc Urban Travel Demand Modeling
Book SynopsisA state-of-the-art approach to urban travel demand modeling Currently used travel forecasting methodology was developed almostthree decades ago, primarily to assess the impacts of large-scalecapital improvement projects, and was not designed to deal withcontemporary urban transportation problems. To be effective today,travel demand models must explicitly represent traveler behavior,must be policy-sensitive, and must be operationally reliable. Urban Travel Demand Modeling: From Individual Choices to GeneralEquilibrium presents an integrated system of models which overhaulthe four traditional phases of travel generation, modal split, tripdistribution, and network assignment. This book shows, for thefirst time, how generalized network equilibrium may be rigorouslyforecast from the optimal travel choices of trip consumerswithout the need to resort to heuristic procedures such asfeedbacks. In addition, models for optimal transportation supplydecisions are integrated with tTable of ContentsModeling Travelers' Decisions as Discrete Choices. Route Choice on Uncongested Networks. Combined Travel Demand Modeling Under Uncongested Conditions. Route Choice Modeling Under Congested Conditions. Combined Travel Demand Modeling Under Congested Conditions. Model Parameter Estimation. Joint Equilibrium Modeling of Activity and Travel Systems. Optimal Transportation Supply. Appendices. Bibliography. Indexes.
£124.15
John Wiley & Sons Inc Bioremediation and Natural Attenuation
Book SynopsisBioremediation and Natural Attenuation: Process Fundamentals and Mathematical Models provides, under one cover, the current methodology needed by groundwater scientists and engineers in their efforts to evaluate contamination problems, to estimate risk to human health and ecosystems, and to design and formulate remediation strategies.Trade Review"…does a very good job of bringing together material form disparate sources…readers new to the field will be well served by it." (Ground Water, March-April 2007) "The topic is important; both theory and state-of-the-art are well discussed…this is an excellent book." (Journal of Hazardous Materials, September 1, 2006) “… a reference book for practitioners, regulators, and researchers dealing with contaminant hydrogeology and correction action.” (Environmental Geology, December 2006)Table of ContentsPreface. 1. Introduction to Bioremediation. 2. Geochemical Attenuation Mechanisms. 3. Biodegradation Principles. 4. Fundamentals of Ground Water Flow and Contaminant Transport Processes. 5. Fate and Transport Equations and Analytical Models for Natural Attenuation. 6. Numerical Modeling of Contaminant Transport, Transformation, and Degradation Processes. 7. Field and Laboratory Techniques to Determine Site-Specific Parameters for Modeling the Fate and Transport of Groundwater Pollutants. 8. Bioremediation Technologies. 9. Performance Assessment and Demonstration of Bioremediation and Natural Attenuation. Appendix A: Chemical Properties of Various Compounds. Appendix B: Free Energy and Thermodynamic Feasibility of Chemical and Biochemical Reactions. Appendix C: Commonly Used Numerical Groundwater Flow and Solute Transport Codes (Modified after Wiedemeier et al., 1999). Appendix D: Nonparametric Statistical Tests for Determining the Effectiveness of Natural Attenuation (after Wisconsin Department of Natural Resources). Appendix E: Critical Values of the Student t-Distribution. Glossary. Index.
£122.35
Wiley DPSM for Modeling Engineering Problems
a huge range and FREE tracked UK delivery on ALL orders.
£141.26
Wiley Graphical Models in Applied Multivariate
Book SynopsisGraphical models----a subset of log--linear models----reveal the interrelationships between multiple variables and features of the underlying conditional independence.Table of ContentsIndependence and Interaction. Independence Graphs. Information Divergence. The Inverse Variance. Graphical Gaussian Models. Graphical Log-Linear Models. Model Selection. Methods for Sparse Tables. Regression and Graphical Chain Models. Models for Mixed Variables. Decompositions and Decomposability. Appendices. References. Author Index. Subject Index.
£277.15
John Wiley & Sons Inc Thermodynamic Optimization FiniteTime
Book SynopsisThe first book to provide a comprehensive treatment integrating finite-time thermodynamics and optimal control, giving an overview of important breakthroughs in the last 20 years. It presents a survey of the optimization technique, including the basics of optimal control theory, and the principal thermodynamic concepts and equations.Table of ContentsMathematical Modeling of Thermodynamic Systems. Optimization Methods. Optimal Control Methods. Limiting Possibilities of Heat-Mechanical Systems with One Reservoir. Heat-Exchange Processes with Minimal Dissipation. Optimization and Estimates of the Limiting Possibilities of Heat-Mechanical Systems with a Number of Reservoirs. Limiting Possibilities of Complex Systems with a Number of Heat-Mechanical Systems. Mass Transfer Processes with Minimal Irreversibility. Thermodynamic Analysis of Separation Processes and Chemical Reactions. Commodity Exchange in Economic Systems. Bibliography. Index.
£376.16
Wiley Nonlinear Modelling of High Frequency Financial
Book SynopsisThis text focuses on the issue of non-linear modelling of high frequency financial data. Non-linearity refers to situations in which there is a high degree of apparent randomness to the way in which a particular financial measure, price, interest rate, or exchange rate moves with time.Table of ContentsHIGH FREQUENCY MODELS IN FINANCE: MOTIVATIONS AND THEORETICAL ISSUES. Modelling with High Frequency Data: A Growing Interest for Financial Economists and Fund Managers (M. Gavridis). High Frequency Foreign Exchange Rates: Price Behavior Analysis and 'True Price' Models (J. Moody & L. Wu). DETECTING NONLINEARITIES IN HIGH FREQUENCY DATA: EMPIRICAL TESTS AND MODELLING IMPLICATIONS. Testing Linearity with Information-Theoretic Statistics and the Bootstrap (F. Acosta). Testing for Linearity: A Frequency Domain Approach (J. Drunat, et al.). Stochastic or Chaotic Dynamics in High Frequency Financial Data (D. Guégan & L. Mercier). F-consistency, De-volatization and Normalization of High Frequency Financial Data (B. Zhou). PARAMETRIC MODELS FOR NONLINEAR FINANCIAL TIME SERIES. High Frequency Financial Time Series Data: Some Stylized Facts and Models of Stochastic Volatility (E. Ghysels, et al.). Modelling Short-term Volatility with GARCH and HARCH Models (M. Dacorogna, et al.). High Frequency Switching Regimes: A Continuous-time Threshold Process (R. Dacco' & S. Satchell). Modelling Burst Phenomena: Bilinear and Autoregressive Exponential Models (J. Drunat, et al.). NON-PARAMETRIC MODELS FOR NONLINEAR FINANCIAL TIME SERIES. Application of Neural Networks to Forecast High Frequency Data: Foreign Exchange (P. Bolland, et al.). An Application of Genetic Algorithms to High Frequency Trading Models: A Case Study (C. Dunis, et al.). High Frequency Exchange Rate Forecasting by the Nearest Neighbours Method (H. Alexandre, et al.). Index.
£94.50
Wiley Evolutionary Algorithms in Engineering and Computer Science
a huge range and FREE tracked UK delivery on ALL orders.
£211.46
Wiley Quantitative Methods for Finan
Book SynopsisQuantitative Methods for Finance and Investments ensures that readers come away from reading it with a reasonable degree of comfort and proficiency in applying elementary mathematics to several types of financial analysis.Trade Review"This excellent text patiently guides the reader through a wide array of mathematics, ranging from elementary matrix algebra to differential and integral calculus. The quantitative methods are illustrated with a rich and captivating assortment of applications to the analysis of portfolios, derivatives, exchange, fixed income instruments, and equities. Undergraduate and MBA-level students who have read this book will feel comfortable with the mathematics in their finance courses and their professors can focus on teaching finance as it should be taught." Kose John, Stern School of Business, New York University <1--end--> "This volume provides a comprehensive review of mathematics which will prove invaluable for students of finance. It is a reference book for the nonmathematician and a clear and concise text that will help fill the gaps in students' knowledge. Although the topic is quantitative methods, the organization, emphasis, applications, and numerous examples are all geared to the student of finance. Having Teall and Hasan on your bookshelf provides an essential safety net for students, teachers, and practitioners." Paul Wachtel, Stern School of Business, New York UniversityTable of ContentsPreface. Acknowledgments. 1. Introduction and Overview:. The Importance of Mathematics in Finance. Mathematical and Computer Modeling in Finance. Money, Securities, and Markets. Time Value, Risk, Arbitrage, and Pricing. The Organization of this Book. 2. Review of Elementary Mathematics: Functions and Operations:. Introduction. Variables, Equations, and Inequalities. Exponents. The Order of Arithmetic Operations and the Rules of Algebra. The Number e. Logarithms. Subscripts. Summations. Double Summations. Products. Factorial Products. Permutations and Combinations. Exercises. Appendix: An Introduction to the ExcelT Spreadsheet. 3. A Review of Elementary Mathematics: Algebra and Solving Equations:. Algebraic Manipulations. The Quadratic Formula. Solving Systems of Equations that Contain Multiple Variables. Geometric Expansions. Functions and Graphs. Exercises. Appendix: Solving Systems of Equations on a Spreadsheet. 4. The Time Value of Money:. Introduction and Future Value. Simple Interest. Compound Interest. Fractional Period Compounding of Interest. Continuous Compounding of Interest. Annuity Future Values. Discounting and Present Value. Present Value of a Series of Cash Flows. Annuity Present Values. Amortization. Perpetuity Models. Single-stage Growth Models. Multiple-stage Growth Models. Exercises. Appendix: Time Value Spreadsheet Applications. 5. Return, Risk, and Co-movement:. Return on Investment. Geometric Mean Return on Investment. Internal Rate of Return. Bond Yields. An Introduction to Risk. Expected Return. Variance and Standard Deviation. Historical Variance and Standard Deviation. Covariance. The Coefficient of Correlation and the Coefficient of Determination. Exercises. Appendix: Return and Risk Spreadsheet Applications. 6. Elementary Portfolio Mathematics:. An Introduction to Portfolio Analysis. Portfolio Return. Portfolio Variance. Diversification and Efficiency. The Market Portfolio and Beta. Deriving the Portfolio Variance Expression. Exercises. 7. Elements of Matrix Mathematics:. An Introduction to Matrices. Matrix Arithmetic. Inverting Matrices. Solving Systems of Equations. Spanning the State Space. Exercises. Appendix: Matrix mathematics on a Spreadsheet. 8. Differential Calculus:. Functions and Limits. Slopes, Derivatives, Maxima, and Minima. Derivatives of Polynomials. Partial and Total Derivatives. The Chain Rule, Product Rule, and Quotient Rule. Logarithmic and Exponential Functions. Taylor Series Expansions. The Method of LaGrange Multipliers. Exercises. Appendix: Derivatives of Polynomials. Appendix: A Table of Rules for Finding Derivatives. Appendix: Portfolio Risk Minimization on a Spreadsheet. 9. Integral Calculus:. Antidifferentiation and the Indefinite Integral. Riemann Sums. Definite Integrals and Areas. Differential Equations. Exercises. Appendix: Rules for Finding Integrals. Appendix: Riemann sums on a spreadsheet. 10. Elements of Options Mathematics:. An Introduction to Stock Options. Binomial Option Pricing: One Time Period. Binomial Option Pricing: Multiple Time Periods. The Black–Scholes Option Pricing Model. Puts and Valuation. Black–Scholes Model Sensitivities. Estimating Implied Volatilities. Exercises. References. Appendix A: Solutions to Exercises. Appendix B: The z-Table. Appendix C: Notation. Appendix D: Glossary. Index.
£72.00
